Interview with DSR CEO and Founder, Eric Olsen

Interviewer: So Eric, tell me how you started Digital System Research, or as you call DSR, and explain what a residue number ALU is.Eric Olsen: well, explaining either could take a day or two, so I really need to keep questions more specific. It’s really the Achilles heel of our work, that is, explaining to others what we’ve done. What we’re finding is explaining and even demonstrating our computer technology isn’t enough. It is evident that working systems exhibiting striking performance gains is required to advance this or any technology.Interviewer: What do you mean exactly?Eric Olsen: D-Wave is a good case in point. D-Wave is the first quantum computer company. I mean there is no way D-Wave can explain how their computer works to the common layperson in an hour, and demonstrating some aspect of quantum computer technology is still not enough. D-Wave needed to bring systems to market before the possibilities of their new computing paradigm could even be appreciated, and soon that appreciation turns into “what ifs” and real customer driven innovation.Interviewer: So you’re saying DSR will have to bring systems to market for us all to know and appreciate the residue ALU?Eric Olsen: Well yes, most likely. But a residue ALU is easier to understand and less complex than a quantum computer! Even if the Rez-9 ALU is not implemented as ASIC, there will be FPGA based versions available for use by academics and interested researchers. Our plan is to introduce our FPGA based Rez-9 development kit to academics and researchers very soon.Interviewer: Besides your Rez-9 development kit, what will it take to bring DSRs technology to market?Eric Olsen: The future of residue computing will depend on many things, including money to fund advanced research and development, to move the technology into ASIC solutions. We know the residue ALU will perform product summations and general summations very efficiently, and we know these operations are becoming increasingly important every day. But it’s doubtful the technology can exist [only] as FPGA solutions. This means significant resources are needed to move the technology forward in terms of practical ASIC applications and solutions.Interviewer: What sort of applications are you talking about?Eric Olsen: We are primarily talking about supercomputer class applications, or intense numerical processing required in many classical as well as emerging applications, like network modeling and cloud based AI. Acceptance of a computer paradigm as an alternative to floating point arithmetic is at a low point I’m afraid. There are significant barriers in bringing our residue ALU technology to the very people who would be using it especially if it can’t perform at the required levels. We’re talking about thousands of processors, and the networks and software to join and use these processors.Interviewer: So the residue ALU cannot perform at the required levels?Eric Olsen: No, I mean yes, of course it can. I mean this is another issue, comparing performance levels. It’s easy to increase performance with more hardware, more cores and better technology regardless of the type of processor, so it becomes difficult comparing a residue ALU to a binary FPU. It’s not just speed, but speed per gate, speed per square millimeter, speed per watt, and speed per core. The reduced latency and higher accuracy of many residue operations may also be a factor. Until more residue architectures are built and more measurements made, it’s too early for blatant comparison. But we’re working with more than faith, we’ve compiled specific data in terms of clocks why a residue ALU is so attractive, but speed isn’t the only thing we have.Interviewer: it sounds like making the residue ALU into a Supercomputer is hard, perhaps you are too late to compete with the likes of Intel and their advanced processors?Eric Olsen: Well it’s true, we’re not going to take over the world. Binary is the most efficient number system for most computer applications and that’s not going to change. Even quantum computers won’t change that fact anytime soon. But residue calculations can be performed in a co-processor attached to a binary CPU which means we off-load certain calculation to our residue ALU. By coupling these devices together, we get a hybrid computer of sorts, and it shows incredible performance that a stand-alone binary CPU cannot match for many numerically intense applications.Interviewer: So the residue ALU will be a co-processor to an Intel CPU?Eric Olsen: Exactly, well we would hope in some future version of an Intel chip, sure. The use of the residue ALU as a co-processor is new since we’ve developed a complete set of operations to sustain general purpose calculations in residue format. That’s a key idea of our technology.Interviewer: You’re saying your residue ALU can perform general purpose calculations, so why not make it a stand-alone CPU? I don’t understand.Eric Olsen: Well we still need a binary computer to interface to the real world. One of the crazy things about residue numbers is you can’t use them directly, they need to be converted back to binary before the results can affect the real world. It’s similar to quantum computing in this respect, I mean, you can’t touch quantum states during their calculation, only after reading quantum state do you get quantum results. So in effect, Quantum states representing answers are converted to binary before they can be used in our physical world. It’s the same thing really.Interviewer: So converting residue ALU results to binary is always needed?Eric Olsen: yes, exactly. It’s a good point because we’ve developed high-speed converters for this purpose. We made several types of converters, both fractional and integer converters. In my view, people tend to have computer science amnesia. I mean back in the early days of computing, converting from decimal to binary and back to decimal was considered a big deal, even hardware was developed for it. But once software routines replaced the difficult chore of conversion everybody forgot about it. Funny thing, these binary to decimal conversions are not efficient so over the years we learned to store and process binary until we needed the data and then we convert the results. That’s exactly the idea behind the residue ALU. Calculations remain in residue until completion and then they’re converted to binary using dedicated conversion instructions built into the ALU.Interviewer: Doesn’t conversion slow down your processing?Eric Olsen: If we didn’t support general purpose operation, then we would have to constantly convert data back and forth, and so yes, that slows things down. Even if some of our operations are slower, like division, being able to do that directly in residue means we can benefit from the speed of other residue operations, like addition and multiplication. We are looking to perform complex operations which are faster in residue than binary despite the cost of conversion. For example, a matrix multiplication falls into that category. For matrix multiplication, the residue ALU is also more accurate in many cases.Interviewer: So you need to have general purpose capability and also high speed conversion to make this practical?Eric Olsen: Exactly. This is why our approach is not related to prior art residue arithmetic.Interviewer: You mentioned before this interview that DSR was rejected for a second time in the Emerging Technologies contest at SC15. So what happened?Eric Olsen: yes, unfortunately we lost again. Our submissions for our new residue based co-processor received mixed reviews. We seem to always get a judge that says yes, a judge that is neutral, and a judge that says no! It’s frustrating to say the least! One of the judges wanted to know how our ALU would help him, and it’s kind of hard to explain that, well, we have “another” general purpose computing paradigm. The funny thing, there isn’t any other general purpose computing paradigm, we’re it! That’s very important in itself.Interviewer: So you’re saying that a second computing paradigm is important, Why?Eric Olsen: yes, very important. We’re re-inventing computer algorithms at an astonishing rate at DSR. For example, we were confronted with having no binary shift mechanism. This appeared to be a CPU killer, perhaps it was for many other researchers. But we discover residue numbers have its own version of shift and it’s powerful and quite flexible. Its opens up new possibilities for routines that you would not tend to advocate using a conventional binary CPU.Interviewer: How does re-inventing computer algorithms become a benefit, since it sounds like a lot of redundant work, like re-inventing all software algorithms?Eric Olsen: Well, again, we humans get into a box pretty easily. I think Steve Jobs was right about fighting Dogma. Shifting is a feature of a fixed radix number system. It’s a great feature, so for a binary CPU we exploit that feature because it’s faster than division. But that’s not the only way to process numbers. Using the techniques at DSR, we’re replacing binary algorithms with residue algorithms, which take advantage of the most efficient residue operations, and some of these are mathematically different because of the underlying number system. There are other reasons to re-invent algorithms since adding two integers in residue is very fast. We can re-visit alternative algorithms to find operations that are efficient in RNS, like addition and multiplication. So we adopt routines which use the most efficient operations available in the RNS ALU.Interviewer: So you can’t use binary algorithms in the residue ALU?Eric Olsen: No, we can execute binary algorithms nearly directly, but they tend to execute more slowly. The main texts on algorithms tend to present the most successful binary algorithms, not the most successful residue algorithms. We don’t even know what exists in the future for residue ALU’s, because we’ve already discovered great efficiencies in terms of alternate algorithms. Early on we discovered that product summations collapse into a series of RNS integer operations, which are very fast. We’re finding if a residue algorithm is not faster, it usually offers some other interesting and useful properties. So residue algorithm design is a fundamental requirement moving forward.Interviewer: So I assume your residue ALU is easier to use than a Quantum computer?Eric Olsen: Well, yes, it is. Both DSR and D-Wave have a similar objective, we must get scientists and engineers to begin to use and program our machines in ways that might be radically different than conventional programming. I would say that programming a residue ALU, while still out of the box, is much easier than programming a quantum computer because a residue ALU is deterministic and is programmed similar to a binary CPU. The out of the box part of residue programming goes away pretty fast if researchers and computer scientists have machines they can program and experiment with. Same goes with quantum computers. Consequently, both companies are striving to develop techniques, compilers, and development interfaces for accelerating the programming and applicability of their machines. That’s why I have so much respect and appreciation for D-Wave.Interviewer: You mentioned that a residue ALU is more than just fast, can you elaborate?Eric Olsen: Yes, we are finding that residue fractions are very accurate. We’ve discovered interesting properties of residue fractions, like being able to support the value one third, or one seventh exactly, which leads to higher accuracy in certain algorithms. For product summations we attain high accuracy because we don’t throw any information away until the final normalization step, so it’s significantly more accurate than using a typical floating point unit. I mean, all things being the same. But of course, that’s never the case.Interviewer: How does supporting the value of one third make your system more accurate?Eric Olsen: well we have to be careful, since we often want to compare number systems of the same fractional range, but that is impossible with binary versus residue, we can only come close, within a single bit. But we do have several indications. For example, according to Newton, he gives an equation to establish an initial guess for his multiplicative inverse algorithm. Using his equation minimizes the error of the result, and surprisingly, the equation requires a perfect divide by 17, or a denominator of one seventeenth. Our residue fractions allow us to represent exactly one seventeenth, and so our initial guess is better. We are still working to prove our suppositions, and we have graphics and curves to demonstrate accuracy of our residue ALU technology. It’s pretty heady stuff.Interviewer: DSR has delayed the release of its Rez-9 ALU a number of times. When will it be ready for release, and what is holding it back?Eric Olsen: Yes, this has been a real issue. First let me first say we are committed to our Rez-9 ALU, and we will get it released so please be patient. Right now, the Rez-9 is fully capable of general purpose processing with the exception of full division. We recently added a beta version integer divide to the ALU and we are verifying its operation. We are working to complete the full fractional divide apparatus. We could have released Rez-9 without the divide instructions, but we could not claim general purpose operation. We plan to ship our Rez-9 development kit by June 2016 with full support for division. Of course, the issue with lack of funding has also slowed our efforts.Interviewer: Well you weren’t kidding about the time needed to explain residue ALU’s, what would you like to say in summary to those who doubt the innovations of your company?Eric Olsen: Well, DSR wants to prove to the world that residue ALU’s are worthwhile, and they will accelerate both the knowledge and art of computation, but it’s clear that we need to do more than demonstrations. So there is not much to say to those that would doubt the innovations because it’s really using the innovation that’s important. So we’ll keep working hard to maximize the benefits of our new technology, get our Rez-9 evaluation kit released, and hopefully, we’ll connect with funding sources that can give us the ability to further advance and deploy our technology into the marketplace. Residue numbers are not perceived so sexy like its Quantum computing counterpart, but it can perform general purpose calculations, and it does so in a new and unique manner, and that is one reason we continue to exploit its strengths.